bootGcRsq: Compute vector of n999 nonlinear Granger causality paths

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

Compute vector of n999 nonlinear Granger causality paths

Description

Maximum entropy bootstrap (meboot) package is used for statistical inference The bootstrap output can be analyzed to estimate an approximate confidence interval on sample-based direction of the causal path. Kernel regression np package options regtype="ll" for local linear, and bwmethod="cv.aic" for AIC-based bandwidth selection are fixed.

Usage

bootGcRsq(x1, x2, px2 = 4, px1 = 4, pwanted = 4, ctrl = 0, n999 = 9)

Arguments

x1	The data vector x1
x2	The data vector x2
px2	number of lags of x2 in the data, default px2=4
px1	number of lags of x1 in the data default px1=4
pwanted	number of lags of both x2 and x1 wanted for Granger causal analysis, default =4
ctrl	data matrix having control variable(s) if any
n999	Number of bootstrap replications (default=9)

Value

out is n999 X 3 matrix for 3 outputs of GcauseX12 resampled

Note

This computation is computer intensive and generally very slow. It may be better to use this function it at a later stage in the investigation, after a preliminary causal determination is already made. The 3 outputs of GauseX12 are two Rsquares and the difference between them after subtracting the second from the first. Col. 1 has (RsqX1onX2), Col.2 has (RsqX2onX1), and Col.3 has dif=(RsqX1onX2 -RsqX2onX1) Note that R-squares are always positive. If dif>0, RsqX1onX2>RsqX2onX1, implying that x2 on RHS performs better that is, x2 –> x1 is the causal path. If dif<0, x1 –> x2 holds. If dif is too close to zero, we may have bidirectional causality x1 <–> x2. The proportion of resamples (out of n999) having dif<0 suggests level of confidence in the conclusion x1 –> x2. The proportion of resamples (out of n999) having dif>0 suggests level of confidence in the conclusion x2 –> x1.

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

References

Vinod, H. D. 'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")}

Zheng, S., Shi, N.-Z., and Zhang, Z. (2012). Generalized measures of correlation for asymmetry, nonlinearity, and beyond. Journal of the American Statistical Association, vol. 107, pp. 1239-1252.

Vinod, H. D. and Lopez-de-Lacalle, J. (2009). 'Maximum entropy bootstrap for time series: The meboot R package.' Journal of Statistical Software, Vol. 29(5), pp. 1-19.

Vinod, H. D. Causal Paths and Exogeneity Tests in Generalcorr Package for Air Pollution and Monetary Policy (June 6, 2017). Available at SSRN: https://www.ssrn.com/abstract=2982128

See Also

See Also GcRsqX12.

Examples

## Not run: 
library(Ecdat);options(np.messages=FALSE);attach(data.frame(MoneyUS))
bootGcRsq(y,m,n999=9) 

## End(Not run)
## Not run: 
library(lmtest); data(ChickEgg);attach(data.frame(ChickEgg))
options(np.messages=FALSE)
b2=bootGcLC(x1=chicken,x2=egg,pwanted=3,px1=3,px2=3,n999=99)
Fn=function(x)quantile(x,prob=c(0.025, 0.975))#confInt
apply(b1,2,Fn)#reports 95 percent confidence interval

## End(Not run)

generalCorr documentation built on Oct. 10, 2023, 1:06 a.m.